Python Guide for Introductory Econometrics for Finance

Python Guide for Introductory Econometrics for Finance
  • eBook:
    Python Guide for Introductory Econometrics for Finance
  • Author:
    Chris Brooks
  • Edition:
    3 edition
  • Categories:
  • Data:
    March 28, 2019
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    256 pages
  • Format:

Book Description
This free software guide for Python with freely downloadable datasets brings the econometric techniques to life, showing readers how to implement the approaches presented in Introductory Econometrics for Finance using this highly popular software package. Designed to be used alongside the main textbook, the guide will give readers the confidence and skills to estimate and interpret their own models while the textbook will ensure that they have a thorough understanding of the conceptual underpinnings.


1. Getting started
2. Data management in Python
3. Simple linear regression - estimation of an optimal hedge ratio
4. Hypothesis testing - Example 1: hedging revisited
5. Estimation and hypothesis testing - Example 2: the CAPM
6. Sample output for multiple hypothesis tests
7. Multiple regression using an APT-style model
8. Quantile regression
9. Calculating principal components
10. Diagnostic testing
11. Constructing ARMA models
12. Forecasting using ARMA models
13. Estimating exponential smoothing models
14. Simultaneous equations modelling
15. The Generalised method of moments for instrumental variables
16. VAR estimation
17. Testing for unit roots
18. Cointegration tests and modelling cointegrated systems
19. Volatility modelling
20. Modelling seasonality in financial data
21. Panel data models
22. Limited dependent variable models
23. Simulation methods
24. The Fama-MacBeth procedure
25. Using extreme value theory for VaR calculation

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